code: def _recall_at_precision(
      precision: Tensor,
      recall: Tensor,
      thresholds: Tensor,
      min_precision: float,
  ) -> Tuple[Tensor, Tensor]:
      max_recall = torch.tensor(
          0.0, device=recall.device, dtype=recall.dtype
      )
      best_threshold = torch.tensor(0)
  
      zipped_len = min(
          t.shape[0] for t in (recall, precision, thresholds)
      )
      zipped = torch.vstack(
          (
              recall[:zipped_len],
              precision[:zipped_len],
              thresholds[:zipped_len],
          )
      ).T
      zipped_masked = zipped[zipped[:, 1] >= min_precision]
      if zipped_masked.shape[0] > 0:
          idx = _lexargmax(zipped_masked)[0]
          max_recall, _, best_threshold = zipped_masked[idx]
      if max_recall == 0.0:
          best_threshold = torch.tensor(
              1e6, device=thresholds.device, dtype=thresholds.dtype
          )
  
      return max_recall, best_threshold
  
file_path: torchmetrics/src/torchmetrics/functional/classification/recall_fixed_precision.py
imports: from typing import Callable, List, Optional, Tuple, Union
  import torch
  from torch import Tensor
  from typing_extensions import Literal
  from torchmetrics.functional.classification.precision_recall_curve import _binary_precision_recall_curve_arg_validation, _binary_precision_recall_curve_compute, _binary_precision_recall_curve_format, _binary_precision_recall_curve_tensor_validation, _binary_precision_recall_curve_update, _multiclass_precision_recall_curve_arg_validation, _multiclass_precision_recall_curve_compute, _multiclass_precision_recall_curve_format, _multiclass_precision_recall_curve_tensor_validation, _multiclass_precision_recall_curve_update, _multilabel_precision_recall_curve_arg_validation, _multilabel_precision_recall_curve_compute, _multilabel_precision_recall_curve_format, _multilabel_precision_recall_curve_tensor_validation, _multilabel_precision_recall_curve_update
  from torchmetrics.utilities.enums import ClassificationTask
label_desc: Use image node_id 2 for calling a global function with example usage: _recall_at_precision(precision, recall, thresholds, min_precision) and returns: max_recall,  best_threshold